Optimization of Energy Consumption by HVAC System in Buildings Using Deep Learning-Based Control Strategies

The building industry, which uses the most electricity, has a significant potential to contribute to energy consumption reduction. Commercial structures use more energy than other types of structures because of their productive and logistic features. In these types of structures, one of the main energy consumers is the HVAC system which comprises of heating, ventilation, and air conditioning, especially in arid conditions. Energy-efficient environment friendly HVAC system conception and execution can significantly lower the use of energy and support ecologically sound growth in business establishments. On the other hand, inadequate implementation of methods for reducing energy use may lead to a decline in the welfare of the environment. Therefore, in order to achieve energy efficiency and maintain the optimum degree of temperature regulation, a comprehensive energy conservation strategy is needed. To accomplish this goal, model predictive control strategy-based methodologies are used in this work. To estimate how much energy will be used in commercial buildings, four deep learning-based methods are utilised: radial basis function networks, multi-layer perceptrons, artificial neural networks, and back propagation neural networks. To further cut down on energy use, four distinct control mechanisms are used. The performance of the suggested solution is examined using performance measures like Mean Absolute Error and Mean Absolute Percentage Error.